Does a linguistic analysis method based on rank order statistics of symbolic sequences discriminate heart rate fluctuation patterns between healthy, aging, and pathologic states?
A novel linguistic analysis method using rank order statistics can robustly discriminate heart rate fluctuation patterns across healthy, aging, and pathologic states, including atrial fibrillation.
Complex physiologic signals may carry unique dynamical signatures that are related to their underlying mechanisms. We present a method based on rank order statistics of symbolic sequences to investigate the profile of different types of physiologic dynamics. We apply this method to heart rate fluctuations, the output of a central physiologic control system. The method robustly discriminates patterns generated from healthy and pathologic states, as well as aging. Furthermore, we observe increased randomness in the heartbeat time series with physiologic aging and pathologic states and also uncover nonrandom patterns in the ventricular response to atrial fibrillation.
Yang et al. (Thu,) studied this question.